The relationship between blood alcohol concentration and crash responsibility for fatally injured drivers.

Data for 595 fatally injured drivers in Texas were analyzed using logistic regression to determine the probability of crash responsibility as a function of blood alcohol concentration (BAC). Three independent panelists rated crash responsibility on a three-point scale, based on information contained in traffic accident reports. High inter-rater reliability was noted. Panel members were in agreement 98% of the time, unanimously assigning full responsibility in 61% of the cases and no responsibility in 31% of the cases. In addition to BAC, time of day and day of week were considered as variables in the regression model. A highly significant (P less than .01) relationship was found between BAC and the probability of crash responsibility. Furthermore, results of the analysis support previous studies that noted a high rate of responsibility among fatally injured drivers, in general, even those with negative BAC test results. Such findings are of importance in evaluating driver impairment at various BACs and for determining other factors that interact with alcohol in driver fatality crashes.     

Factors associated with alcohol and drug use among traffic crash victims in southern Brazil

Objective

To investigate the prevalence of and factors associated with alcohol- or drug-related traffic crashes (TC) in a sample of TC victims who were admitted to the two emergency rooms of Porto Alegre in southern Brazil.

Methods

A cross-sectional study with consecutive samples was used. Victims of non-fatal TCs (as drivers, passengers or pedestrians) who had presented at emergency rooms during the 45 days of data collection were selected. Subjects participated in a structured interview, were breathalyzed and underwent salivary drug testing. A multinomial logistic regression model was used to verify factors associated with alcohol or drug use.

Results

Of the 609 victims who participated in the interview, 72% were male, and the median age was 29 years (interquartile range 23.0–40.0 years). The drivers were mostly men (p < 0.001), with a higher binge drinking rate (p = 0.003) and marijuana use (p = 0.005) than seen in pedestrian and passengers. The prevalence of a positive blood alcohol concentration (BAC) ranged from 7.8% among the drivers to 9.2% among the pedestrians (p = 0.861), and the cannabis prevalence was 13.3% among the drivers. The variables associated with an alcohol-related accident were binge drinking in the prior 12 months (OR 2.4; CI 95% 1.1–5.1) and coming from a party/bar (OR 8.7; CI 95% 2.8–26.7). Alcohol abuse or dependence increased by 5.2-fold the chance of another substance-related TC.

Conclusion

The large number of individuals found in TC-related emergency room visits in a short time frame is evidence of the Brazilian epidemic of TC. The data showed that alcohol abuse or dependence also increases the risk of intoxication by other drugs, and they point to alcohol and drug use as a major problem requiring specific TC-related public policies and law enforcement.     

Diseases, consumption of medicines and responsibility for a road crash: a case-control study

The role of medical conditions in crashes is a topic of public debate. Some studies suggest that there has been a reduction in road traffic crashes subsequent to the medical restrictions introduced on drivers with medical deficiencies. As in today's society the car is an important factor for independence and socialization, it seems important to consider whether diseases or consumption of drugs increase the risk of causing a road crash in comparison to well-known major crash risk factors. A case-control study was conducted (733 injured drivers). The cases were subjects who were partly or totally responsible for their crash. The 304 controls were the non-responsible drivers. Diseases and medicine consumption were analyzed using logistic regression models. Cases were characterized by a higher percentage of young men. They were more frequently affected by fatigue, as were subjects who had consumed alcohol. A higher risk in subjects suffering from hypertension is observed (adjusted odds ratio [adjOR]=3.82; 95%CI=[1.42-10.24]). An association between antidepressant consumption and responsibility appeared (adjOR=3.61; 95%CI=[1.30-10.03]). CONCLUSION: Medical factors associated with responsibility were arterial hypertension and antidepressant consumption. Other medical conditions do not seem to play a preponderant role comparing to individual behaviours.     

Spatial and temporal visualisation techniques for crash analysis

Understanding the underlying structure of single vehicle crashes (SVCs) is essential for improving safety on the roads. Past research has found that SVCs tend to cluster both spatially and temporally. However, limited research has been conducted to investigate the interaction between the location of SVCs and the time they occur, especially at different levels of scales or spatial extents.This paper applied spatial, temporal and spatio-temporal techniques to investigate patterns of SVCs in Western Australia between 1999 and 2008, at different levels of scale. Spider graphs were adapted to identify temporal patterns of vehicle crashes at two different levels of scales: daily and weekly with respect to their causes. The spatial structures of vehicle crashes were analysed using Kernel Density Estimation analysis at three different scales: West Australia, Metropolitan area, and Perth Local Government Area (LGA). These are illustrated using spatial zooming theory. Comap was then used to demonstrate the spatio-temporal interaction effect on vehicle crashes. The results show significant differences in spatio-temporal patterns of SVCs for various crash causes. The techniques used here have the potential to help decision makers in developing effective road safety strategies.     

An evaluation scheme for assessing the worth of automatically generated design alternatives

This paper introduces a tool called the designer preference modeler (DPM) that analyzes the designer’s decision making during concept evaluation, and constructs a designer preference model to be used for evaluation of automatically generated design alternatives. The method is based on establishing an interaction between a designer and a computational synthesis tool during conceptual design. The synthesis software generates design alternatives using a catalog of design knowledge formulated as grammar rules which describe how electromechanical designs are built. DPM carefully selects a set from these alternatives and presents it to the designer for evaluation. The designer’s evaluations are translated into a preference model that is subsequently used to search the solution space for best designs. Application of the method to the design of a consumer product shows DPM’s range of capabilities.     

Generalized nonlinear models for rear-end crash risk analysis

A generalized nonlinear model (GNM)-based approach for modeling highway rear-end crash risk is formulated using Washington State traffic safety data. Previous studies majorly focused on causal factor identification and crash risk modeling using Generalized linear Models (GLMs), such as Poisson regression, Logistic regression, etc. However, their basic assumption of a generalized linear relationship between the dependent variable (for example, crash rate) and independent variables (for example, contribute factors to crashes) established via a link function can be often violated in reality. Consequently, the GLM-based modeling results could provide biased findings and conclusions. In this research, a GNM-based approach is developed to utilize a nonlinear regression function to better elaborate non-monotonic relationships between the independent and dependent variables using the rear end accident data collected from 10 highway routes from 2002 through 2006. The results show for example that truck percentage and grade have a parabolic impact: they increase crash risks initially, but decrease them after the certain thresholds. Such non-monotonic relationships cannot be captured by regular GLMs which further demonstrate the flexibility of GNM-based approaches in the nonlinear relationship among data and providing more reasonable explanations. The superior GNM-based model interpretations help better understand the parabolic impacts of some specific contributing factors for selecting and evaluating rear-end crash safety improvement plans.     

The effects of combining sanctions and rehabilitation for driving under the influence: An evaluation of the New Jersey alcohol countermeasures program

In contrast to many other state Driving under the Influence (DUI) programs developed in the United States in the 1970s as alternatives to traditional sanctions, the New Jersey Alcohol Countermeasures Program combined sanctions with mandatory education/rehabilitation for offenders. Three components were evaluated: DUI education, “treatment,” and Alcoholics Anonymous. For 2,734 first and repeat offenders participating in this program between 1979 and 1982, the program was effective in reducing DUI recidivism for program completers (66% while licensed and 51% while suspended) compared with noncompleters, but it was less effective in reducing subsequent moving violations while licensed (20% compared with noncompleters) and accidents while licensed (18% compared with noncompleters). Completers had higher rates of violations and accidents while suspended (9% compared with noncompleters). A small group of repeat offenders, missassigned to DUI education, had higher post conviction rates of negative driving events than those of comparable offenders assigned to “treatment” or Alcoholics Anonymous, indicating that for these offenders the latter interventions were more effective.     

Application of finite mixture models for vehicle crash data analysis

Developing sound or reliable statistical models for analyzing motor vehicle crashes is very important in highway safety studies. However, a significant difficulty associated with the model development is related to the fact that crash data often exhibit over-dispersion. Sources of dispersion can be varied and are usually unknown to the transportation analysts. These sources could potentially affect the development of negative binomial (NB) regression models, which are often the model of choice in highway safety. To help in this endeavor, this paper documents an alternative formulation that could be used for capturing heterogeneity in crash count models through the use of finite mixture regression models. The finite mixtures of Poisson or NB regression models are especially useful where count data were drawn from heterogeneous populations. These models can help determine sub-populations or groups in the data among others. To evaluate these models, Poisson and NB mixture models were estimated using data collected in Toronto, Ontario. These models were compared to standard NB regression model estimated using the same data. The results of this study show that the dataset seemed to be generated from two distinct sub-populations, each having different regression coefficients and degrees of over-dispersion. Although over-dispersion in crash data can be dealt with in a variety of ways, the mixture model can help provide the nature of the over-dispersion in the data. It is therefore recommended that transportation safety analysts use this type of model before the traditional NB model, especially when the data are suspected to belong to different groups.     

Statistical implications of using moving violations to determine crash responsibility in young driver crashes

Traditional methods for determining crash responsibility – most commonly moving violation citations – may not accurately characterize at-fault status among crash-involved drivers given that: (1) issuance may vary by factors that are independent of fault (e.g., driver age, gender), and (2) these methods do not capture driver behaviors that are not illegal but still indicative of fault. We examined the statistical implications of using moving violations to determine crash responsibility in young driver crashes by comparing it with a method based on crash-contributing driver actions. We selected all drivers in police-reported passenger-vehicle crashes (2010–2011) that involved a New Jersey driver <21 years old (79,485 drivers < age 21, 61,355 drivers ≥ age 21). For each driver, crash responsibility was determined from the crash report using two alternative methods: (1) issuance of a moving violation citation; and (2) presence of a driver action (e.g., failure to yield, inattention). Overall, 18% of crash-involved drivers were issued a moving violation while 50% had a driver action. Only 32.2% of drivers with a driver action were cited for a moving violation. Further, the likelihood of being cited given the presence of a driver action was higher among certain driver subgroups—younger drivers, male drivers, and drivers in single-vehicle and more severe crashes. Specifically among young drivers, those driving at night, carrying peer passengers, and having a suspended or no license were more often cited. Conversely, fatally-injured drivers were almost never cited. We also demonstrated that using citation data may lead to statistical bias in the characterization of at-fault drivers and of quasi-induced exposure measures. Studies seeking to accurately determine crash responsibility should thoughtfully consider the potential sources of bias that may result from using legal culpability methods. For many studies, determining driver responsibility via the identification of driver actions may yield more accurate characterizations of at-fault drivers.     

汽车磁流变碰撞缓冲器的优化设计与分析

为改进汽车碰撞安全性能,提出一种基于磁流变技术的新型汽车智能碰撞缓冲器的设计方案.针对现有磁流变缓冲器高速环境工作中存在设计理论不足的问题,提出以宾汉塑性非线性流体修正模型为理论基础,将优化目标转化为响应时间、最大阻尼力和阻尼力的动态范围三个指标的加权和,运用遗传算法对所设计的磁流变缓冲器结构参数进行优化的方法.优化结果表明,所提出的设计和优化方法使磁流变缓冲器具有更好的磁路结构,更短的响应时间.     

An empirical analysis of farm vehicle crash injury severities on Iowa's public road system

Farm vehicle crashes are a major safety concern for farmers as well as all other users of the public road system in agricultural states. Using data on farm vehicle crashes that occurred on Iowa's public roads between 2004 and 2006, we estimate a multinomial logit model to identify crash-, farm vehicle-, and driver-specific factors that determine farm vehicle crash injury severity outcomes. Estimation findings indicate that there are crash patterns (rear-end manner of collision; single-vehicle crash; farm vehicle crossed the centerline or median) and conditions (obstructed vision and crash in rural area; dry road, dark lighting, speed limit 55 mph or higher, and harvesting season), as well as farm vehicle and driver-contributing characteristics (old farm vehicle, young farm vehicle driver), where targeted intervention can help reduce the severity of crash outcomes. Determining these contributing factors and their effect is the first step to identifying countermeasures and safety strategies in a bid to improve transportation safety for all users on the public road system in Iowa as well as other agricultural states.     

Using support vector machine models for crash injury severity analysis

The study presented in this paper investigated the possibility of using support vector machine (SVM) models for crash injury severity analysis. Based on crash data collected at 326 freeway diverge areas, a SVM model was developed for predicting the injury severity associated with individual crashes. An ordered probit (OP) model was also developed using the same dataset. The research team compared the performance of the SVM model and the OP model. It was found that the SVM model produced better prediction performance for crash injury severity than did the OP model. The percent of correct prediction for the SVM model was found to be 48.8%, which was higher than that produced by the OP model (44.0%). Even though the SVM model may suffer from the multi-class classification problem, it still provides better prediction results for small proportion injury severities than the OP model does.     

An approach for assessing human decision reliability

This paper presents a method to study human reliability in decision situations related to nuclear power plant disturbances. Decisions often play a significant role in handling of emergency situations. The method may be applied to probabilistic safety assessments (PSAs) in cases where decision making is an important dimension of an accident sequence. Such situations are frequent e.g. in accident management. In this paper, a modelling approach for decision reliability studies is first proposed. Then, a case study with two decision situations with relatively different characteristics is presented. Qualitative and quantitative findings of the study are discussed. In very simple decision cases with time pressure, time reliability correlation proved out to be a feasible reliability modelling method. In all other decision situations, more advanced probabilistic decision models have to be used. Finally, decision probability assessment by using simulator run results and expert judgement is presented.     

The contribution of alcohol to night time crash risk and other risks of night driving

Many studies show that driving at night is more risky in terms of crash involvements per distance travelled than driving during the day. The reasons for this include the more prevalent use of alcohol by drivers at night, the effects of fatigue on the driving task and the risk associated with reduced visibility. Although the consumption of alcohol prior to driving occurs most commonly at night, drink-driving is not inherently a night time risk factor. This study decomposes the New Zealand risk of driving at night into risk associated with alcohol and risk associated with inherently night time factors. The overall risk associated with alcohol use by drivers was shown to decrease with increasing age for the most risky situation analysed (male drivers on weekend nights). Given the levels of drinking and driving on weekend nights, the overall effect of alcohol was shown to contribute almost half of weekend night time risk for drivers aged under 40 on lower volume roads, but to contribute little to overall risk on higher-volume roads, consistent with other research showing that higher-volume roads are not favoured by drinking drivers. Risk at night relative to risk during the day (excluding risk associated with drinking and driving) was shown to decrease with age. Roads with illumination at night are less risky at night relative to during the day than roads without illumination. The risks estimated in this paper reflect the behaviour of the road users studied and their prevalence on the roads under the conditions analysed.     

Quantitative evaluation of the effects of poly-vinyl alcohol on supercooling phenomena of water

The effect of poly-vinyl alcohols (PVAs) on the supercooling of water was investigated experimentally for varying sample volumes and degrees of polymerization and saponification. A statistical analysis was carried out on the experimental results and a quantitative evaluation of the inhibition effect of PVA on release of the supercooled state was attempted by introducing a coefficient representing the inhibition effect of PVA. Experimental results and the values calculated from the statistical analysis agreed well and it was found the statistical analysis could be applied to the freezing of supercooled water containing PVA. Moreover, the effects of the PVA on the release of the supercooled state were evaluated quantitatively, using the statistical analysis. The effect of the degree of polymerization is insignificant but it was found that the inhibition effect of PVA increased with increasing degrees of saponification.     

Validating crash locations for quantitative spatial analysis: a GIS-based approach

In this paper, the spatial variables of the crash database in Hong Kong from 1993 to 2004 are validated. The proposed spatial data validation system makes use of three databases (the crash, road network and district board databases) and relies on GIS to carry out most of the validation steps so that the human resource required for manually checking the accuracy of the spatial data can be enormously reduced. With the GIS-based spatial data validation system, it was found that about 65-80% of the police crash records from 1993 to 2004 had correct road names and district board information. In 2004, the police crash database contained about 12.7% mistakes for road names and 9.7% mistakes for district boards. The situation was broadly comparable to the United Kingdom. However, the results also suggest that safety researchers should carefully validate spatial data in the crash database before scientific analysis.     

Computational and experimental crash analysis of the road safety barrier

The paper describes the computational analysis and experimental crash tests of a new road safety barrier. The purpose of this research was to develop and evaluate a full-scale computational model of the road safety barrier for use in crash simulations and to further compare the computational results with real crash test data. The impact severity and stiffness of the new design have been evaluated with the dynamic nonlinear elasto-plastic analysis of the three-dimensional road safety barrier within the framework of the finite element method with LS-DYNA code. Comparison of computational and experimental results proved the correctness of the computational model. The tests have also shown that the new safety barrier assures controllable crash energy absorption which in turn increases the safety of vehicle occupants.     

An example of the usefulness of joinpoint trend analysis for assessing changes in traffic safety policies

Aim

To show the usefulness of joinpoint trend analysis for assessing changes in traffic safety policies.

Methods

Trends in driver death rates from traffic injury stratified by alcohol involvement in Spain during 2001–2011 were characterized with joinpoint regression to observe how changes in these rates match in time with the implementation of traffic regulations and interventions.

Results

Both alcohol-related (blood alcohol concentration >0.3 g/l) and non-alcohol-related rates decreased similarly (10–11% annually) during 2001–2011, although the former showed an earlier (2003 joinpoint) acceleration of the downward trend than the latter (2006 joinpoint); both joinpoints matched in time with relevant road safety interventions.

Conclusions

These results, which are consistent with results from previous analytical studies, show that joinpoint trend analysis, although not valid for causal inference, is useful for assessing changes in traffic safety policies. This methodology can be easily extended to other risk factors, such as excessive speeding or not wearing a seat belt.